14 research outputs found
Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain
In this paper, we study the problem of moving sensors on a line to form a
barrier coverage of a specified segment of the line such that the maximum
moving distance of the sensors is minimized. Previously, it was an open
question whether this problem on sensors with arbitrary sensing ranges is
solvable in polynomial time. We settle this open question positively by giving
an time algorithm. For the special case when all sensors have
the same-size sensing range, the previously best solution takes time.
We present an time algorithm for this case; further, if all
sensors are initially located on the coverage segment, our algorithm takes
time. Also, we extend our techniques to the cycle version of the problem
where the barrier coverage is for a simple cycle and the sensors are allowed to
move only along the cycle. For sensors with the same-size sensing range, we
solve the cycle version in time, improving the previously best
time solution.Comment: This version corrected an error in the proof of Lemma 2 in the
previous version and the version published in DCG 2013. Lemma 2 is for
proving the correctness of an algorithm (see the footnote of Page 9 for why
the previous proof is incorrect). Everything else of the paper does not
change. All algorithms in the paper are exactly the same as before and their
time complexities do not change eithe
On the Displacement for Covering a dimensional Cube with Randomly Placed Sensors
Consider sensors placed randomly and independently with the uniform
distribution in a dimensional unit cube (). The sensors have
identical sensing range equal to , for some . We are interested in
moving the sensors from their initial positions to new positions so as to
ensure that the dimensional unit cube is completely covered, i.e., every
point in the dimensional cube is within the range of a sensor. If the
-th sensor is displaced a distance , what is a displacement of minimum
cost? As cost measure for the displacement of the team of sensors we consider
the -total movement defined as the sum , for some
constant . We assume that and are chosen so as to allow full
coverage of the dimensional unit cube and .
The main contribution of the paper is to show the existence of a tradeoff
between the dimensional cube, sensing radius and -total movement. The
main results can be summarized as follows for the case of the dimensional
cube.
If the dimensional cube sensing radius is and
, for some , then we present an algorithm that uses
total expected movement (see Algorithm 2 and
Theorem 5).
If the dimensional cube sensing radius is greater than
and is a natural
number then the total expected movement is
(see Algorithm 3 and Theorem 7).
In addition, we simulate Algorithm 2 and discuss the results of our
simulations
Movement-efficient Sensor Deployment in Wireless Sensor Networks
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Two key issues in MWSNs - energy consumption, which
is dominated by sensor movement, and sensing coverage - have attracted plenty
of attention, but the interaction of these issues is not well studied. To take
both sensing coverage and movement energy consumption into consideration, we
model the sensor deployment problem as a constrained source coding problem. %,
which can be applied to different coverage tasks, such as area coverage, target
coverage, and barrier coverage. Our goal is to find an optimal sensor
deployment to maximize the sensing coverage with specific energy constraints.
We derive necessary conditions to the optimal sensor deployment with (i) total
energy constraint and (ii) network lifetime constraint. Using these necessary
conditions, we design Lloyd-like algorithms to provide a trade-off between
sensing coverage and energy consumption. Simulation results show that our
algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure
Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy
consumption, and connectivity, have attracted plenty of attention, but the
interaction of these factors is not well studied. To take all the three factors
into consideration, we model the sensor deployment problem as a constrained
source coding problem. %, which can be applied to different coverage tasks,
such as area coverage, target coverage, and barrier coverage. Our goal is to
find an optimal sensor deployment (or relocation) to optimize the sensing
quality with a limited communication range and a specific network lifetime
constraint. We derive necessary conditions for the optimal sensor deployment in
both homogeneous and heterogeneous MWSNs. According to our derivation, some
sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these
necessary conditions, we design both centralized and distributed algorithms to
provide a flexible and explicit trade-off between sensing uncertainty and
network lifetime. The proposed algorithms are successfully extended to more
applications, such as area coverage and target coverage, via properly selected
density functions. Simulation results show that our algorithms outperform the
existing relocation algorithms
Optimal online and offline algorithms for robot-assisted restoration of barrier coverage
Cooperation between mobile robots and wireless sensor networks is a line of
research that is currently attracting a lot of attention. In this context, we
study the following problem of barrier coverage by stationary wireless sensors
that are assisted by a mobile robot with the capacity to move sensors. Assume
that sensors are initially arbitrarily distributed on a line segment
barrier. Each sensor is said to cover the portion of the barrier that
intersects with its sensing area. Owing to incorrect initial position, or the
death of some of the sensors, the barrier is not completely covered by the
sensors. We employ a mobile robot to move the sensors to final positions on the
barrier such that barrier coverage is guaranteed. We seek algorithms that
minimize the length of the robot's trajectory, since this allows the
restoration of barrier coverage as soon as possible. We give an optimal
linear-time offline algorithm that gives a minimum-length trajectory for a
robot that starts at one end of the barrier and achieves the restoration of
barrier coverage. We also study two different online models: one in which the
online robot does not know the length of the barrier in advance, and the other
in which the online robot knows the length of the barrier. For the case when
the online robot does not know the length of the barrier, we prove a tight
bound of on the competitive ratio, and we give a tight lower bound of
on the competitive ratio in the other case. Thus for each case we give an
optimal online algorithm.Comment: 20 page
Maximizing Barrier Coverage Lifetime with Mobile Sensors
Sensor networks are ubiquitously used for detection and tracking and as a
result covering is one of the main tasks of such networks. We study the problem
of maximizing the coverage lifetime of a barrier by mobile sensors with limited
battery powers, where the coverage lifetime is the time until there is a
breakdown in coverage due to the death of a sensor. Sensors are first deployed
and then coverage commences. Energy is consumed in proportion to the distance
traveled for mobility, while for coverage, energy is consumed in direct
proportion to the radius of the sensor raised to a constant exponent. We study
two variants which are distinguished by whether the sensing radii are given as
part of the input or can be optimized, the fixed radii problem and the variable
radii problem. We design parametric search algorithms for both problems for the
case where the final order of the sensors is predetermined and for the case
where sensors are initially located at barrier endpoints. In contrast, we show
that the variable radii problem is strongly NP-hard and provide hardness of
approximation results for fixed radii for the case where all the sensors are
initially co-located at an internal point of the barrier
Weak coverage of a rectangular barrier
Assume n wireless mobile sensors are initially dispersed in an ad hoc manner in a rectangular region. They are required to move to final locations so that they can detect any intruder crossing the region in a direction parallel to the sides of the rectangle, and thus provide weak bar-rier coverage of the region. We study three optimization problems related to the movement of sensors to achieve weak barrier coverage: minimizing the number of sensors moved (MinNum), minimizing the average distance moved by the sensors (MinSum), and minimizing the maximum distance moved by the sensors (